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Explaining cube measures through Intentional Analytics
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AI
DOI：10.1016/j.is.2023.102338.png)
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Abstract
En 中文
The Intentional Analytics Model (IAM) has been devised to couple OLAP and analytics by (i) letting users express their analysis intentions on multidimensional data cubes and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of models (e.g., correlations). Five intention operators were proposed to this end; of these, describe and assess have been investigated in previous papers. In this work we enrich the IAM picture by focusing on the explain operator, whose goal is to provide an answer to the user asking why does measure m show these values?; specifically, we consider models that explain m in terms of one or more other measures. We propose a syntax for the operator and discuss how enhanced cubes are built by (i) finding the relationship between m and the other cube measures via regression analysis and cross-correlation, and (ii) highlighting the most interesting one. Finally, we test the operator implementation in terms of efficiency and effectiveness.
Keywords:
Data cube
OLAP
Analytics
Correlation
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